Digital pathology involves the use of computers to assist pathologists in grading tissue specimens. For example, a tissue sample for breast carcinoma diagnosis typically takes an expert five minutes or more to grade. Several studies have demonstrated low agreement among pathologists' grading of the same case, questioning the objectivity of their diagnosis. A successful system may assist the pathologist in diagnosis, helping to achieve more reproducible results at lower cost.
The identification of epithelial tissue is important for diagnosis of breast and gastric cancer, because cell nuclei density around the epithelium reaches high levels, which trigger a false alarm for malignancy. These areas must be ruled out from nuclei density estimates in order to produce histological statistics of diagnostic value.
In the case of breast and gastric cancer, a tissue sample may be taken from a patient, sliced, stained with hematoxylin and eosin dyes, and imaged by a digital scanner with microscopic resolution. The problem is to distinguish the portions of the tissue that lie within epithelial layers from those that do not.
The prior art has addressed the problem of distinguishing portions of tissue in a couple of methods. In one method, a linear classifier (trained with a linear neural network), is used to classify each pixel of the scanned image into one of four tissue categories according to its color. Then hand-designed heuristics are used to find the boundaries of the epithelium. In another method, color segmentation is first applied to the image. Each segment is then classified as one of several different objects according to hand-designed heuristics based on its color, some basic shape features, and the earlier classification of nearby segments. One of the classes available is “epithelial nuclei”, which are defined as segments that have a hematoxylin color and are neither round enough to look like “stromal nuclei” nor large and dark enough to look like “apoptotic nuclei”.
Unfortunately, the above methods have limited flexibility. The methods based on hand-designed heuristics, require a new set of rules to be designed for each pattern that is to be recognized. In addition, the color segmentation method can only recognize nuclear material as part of the epithelial layer It cannot recognize other materials, such as the mucinous cell bodies of goblet cells, that should be properly regarded as part of the epithelial layer.
Accordingly, an improved and more flexible apparatus/method is needed for automatically distinguishing the portions of the tissue that lie within epithelial layers from those that do not.